Distributed Resource Allocation for Relay-Aided Device-to-Device\n Communication: A Message Passing Approach
Bibliographic record
Abstract
Device-to-device (D2D) communication underlaying cellular wireless networks\nis a promising concept to improve user experience and resource utilization by\nallowing direct transmission between two cellular devices. In this paper,\nperformance of network-assisted D2D communication is investigated where D2D\ntraffic is carried through relay nodes. Considering a multi-user and\nmulti-relay network, we propose a distributed solution for resource allocation\nwith a view to maximizing network sum-rate. An optimization problem is\nformulated for radio resource allocation at the relays. The objective is to\nmaximize end-to-end rate as well as satisfy the data rate requirements for\ncellular and D2D user equipments under total power constraint. Due to\nintractability of the resource allocation problem, we propose a solution\napproach using message passing technique where each user equipment sends and\nreceives information messages to/from the relay node in an iterative manner\nwith the goal of achieving an optimal allocation. Therefore, the computational\neffort is distributed among all the user equipments and the corresponding relay\nnode. The convergence and optimality of the proposed scheme are proved and a\npossible distributed implementation of the scheme in practical LTE-Advanced\nnetworks is outlined. The numerical results show that there is a distance\nthreshold beyond which relay-aided D2D communication significantly improves\nnetwork performance with a small increase in end-to-end delay when compared to\ndirect communication between D2D peers.\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".